AI-enabled SaaS features often get reviewed as isolated prompts or model calls, but the meaningful risk usually sits in the surrounding application workflow. Permissions, tenant boundaries, tool execution, retrieval pipelines, and output handling all matter more than the model alone.
Where risk tends to show up
The most common problem is over-trusting model output. If an LLM can influence workflow branching, tool selection, or data retrieval without strong validation, prompt injection and indirect input attacks can turn into practical impact. In multi-tenant products, that often means cross-tenant data exposure, unwanted actions, or unsafe escalation paths.
Another recurring issue is weak separation between user intent and system authority. An assistant may be presented as a helpful product feature, but under the hood it might have broad access to internal APIs, administrative actions, or reporting systems. That creates a large trust gap if controls are not explicit.
What to review before launch
- How data enters the model and how untrusted content is labeled
- Whether retrieved context can influence privileged actions
- Which tools are available to the model and what they can reach
- How outputs are validated before they affect users or systems
- What logging and evaluation exist for unsafe or unexpected behavior
Good patterns
The strongest implementations treat the model as an untrusted component operating inside well-defined boundaries. That means constrained tools, explicit authorization checks outside the model, scoped retrieval, and graceful failure when confidence is low.
Security reviews are most effective when they are done at the feature and workflow level. Teams that only test prompts usually miss the higher-value failure modes.